Screening mammography has limited sensitivity and specificity. Digital Breast Tomosynthesis (DBT) is an emerging modality that has been shown to significantly improve the detection and characterization of soft- tissue lesions. However, initial studies have shown that subtle microcalcification (MC) clusters, which are often the only sign of early breast cancer, can be difficult to visualize in DBT. Some have suggested that DBT be used in parallel with FFDM in screening, (i.e., adding one- or two-view DBT to the two-view FFDMs so that FFDM could be used for MC detection while DBT could be used for mass detection). This approach would increase imaging costs, reading time, and patient dose, which are all major concerns with regards to introducing DBT into clinical practice. The main goal of the proposed Partnership between the University of Michigan Computer-Aided Diagnosis Research Laboratory (UM) and GE Global Research (GE) is to develop an integrated practical approach to resolving the MC visualization and detection problems in DBT without increasing patient dose, thereby facilitating the eventual replacement of FFDM by DBT. To achieve this goal, we propose two Specific Aims: (SA1) to develop specially designed MC enhancing methods to improve human and machine visualization of MCs in DBT and develop a computer-aided detection (CAD) system to highlight significant MC clusters, and (SA2) to implement the developed MC-enhancing and CAD reading tools in a DBT workstation and conduct observer performance studies to compare MC detection in DBT with that in FFDM. The following tasks will be conducted to accomplish the specific aims: (1) perform phantom studies to determine the best set of image acquisition parameters for data collection, (2) collect a database of human subject DBTs for development of algorithms and observer study, (3) develop lesion-specific reconstruction and MC enhancing methods to improve the visibility of MCs in DBT for radiologist's reading and computerized detection, (4) develop computer-vision methods to detect MC candidates, (5) develop MC analysis method to reduce false positives (FPs) and insignificant CAD marks, (6) design two-view analysis to further reduce FPs, (7) study dependence of MC detection on reconstruction methods and tomosynthesis acquisition parameters, and (8) design a DBT workstation implemented with the MC-enhancing and CAD- assisted tools to highlight significant MCs for radiologist's reading. We hypothesize that the specially designed DBT display system can assist radiologists in detection of MCs in DBT with accuracy at least comparable to that in FFDM. To test this hypothesis, we will (9) conduct observer ROC studies to compare the detection accuracy of MCs under three conditions: (a) two-view DBT without CAD vs. two-view FFDM without CAD, (b) two-view DBT with CAD vs. two-view FFDM with CAD, and (c) a special protocol of CC-view FFDM plus MLO-view DBT with CAD vs. two-view FFDM with CAD.